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A visible‐light induced approach has enabled the synthesis of 3‐alkyl indole from ynones via Csp3−H bond functionalization. This protocol proceeded under base‐free conditions andat room temperature, providing a series of 3‐alkyl indole motifs in 39% to 90% yields (44 examples). Preliminary mechanistic studies suggest that a radical process should be involved in this transformation. [ABSTRACT FROM AUTHOR]

Background: Feeding intolerance (FI) is a significant concern in the care of preterm infants, impacting their growth and development. We previously reported that FI is linked to lower fecal calprotectin (FC) levels. This study aims to explore the postnatal dynamics and interplay between microbiota, metabolic profiles, and host immunity in preterm infants with and without FI. Methods: Infants with gestational age <32 weeks or birth weight <1500 g were enrolled at the Children's Hospital of Fudan University between January 2018 and October 2020. Weekly fecal samples were analyzed for bacterial profiling, metabolome, and calprotectin levels, exploring their longitudinal development and interrelationships. Results: Of the 118 very preterm infants studied, 48 showed FI. These infants experienced an interrupted microbial–immune trajectory, particularly at 3–4 weeks of age, marked by a reduced bacterial abundance, alpha diversity, and FC levels. Metabolic changes in FI were pronounced between 3 and 6 weeks. Pantothenic acid and two polyamine metabolites were closely associated with bacterial abundance and FC levels and negatively correlated with the duration to attain full enteral feeding. Conclusions: FI infants demonstrated compromised microbiome–immune interactions, potentially influenced by specific metabolites. This research underscored the importance of early microbial and metabolic development in the pathogenesis of FI in very preterm infants. [ABSTRACT FROM AUTHOR]

N6-methyladenosine (m6A), the most prevalent mRNA modification, has an important function in diverse biological processes. However, the involvement of m6A in allergic asthma and macrophage homeostasis remains largely unknown. Here we show that m6A methyltransferases METTL3 is expressed at a low level in monocyte-derived macrophages from childhood allergic asthma patients. Conditional knockout of Mettl3 in myeloid cells enhances Th2 cell response and aggravates allergic airway inflammation by facilitating M2 macrophage activation. Loss and gain functional studies confirm that METTL3 suppresses M2 macrophage activation partly through PI3K/AKT and JAK/STAT6 signaling. Mechanistically, m6A-sequencing shows that loss of METTL3 impairs the m6A-YTHDF3-dependent degradation of PTX3 mRNA, while higher PTX3 expression positively correlates with asthma severity through promoting M2 macrophage activation. Furthermore, the METTL3/YTHDF3-m6A/PTX3 interactions contribute to autophagy maturation in macrophages by modulating STX17 expression. Collectively, this study highlights the function of m6A in regulating macrophage homeostasis and identifies potential targets in controlling allergic asthma.The function of METTL3 and RNA methylation is important in various biological processes. Here the authors show that METTL3 is reduced in childhood asthma patients and that conditional knockout of Mettl3 in mouse myeloid cells enhances Th2 response and allergic asthma associated with changes in macrophage function. [ABSTRACT FROM AUTHOR]

Paracetamol overdose is a leading cause of acute liver failure that can prove fatal. Establishing paracetamol concentration accurately and quickly is critical. Current detection methods are invasive, time-consuming and/or expensive. Non-invasive, rapid and cost-effective techniques are urgently required. To address this challenge, a novel approach, called Paper-Arrow Mass Spectrometry (PA-MS) has been developed. This technique combines sample collection, extraction, enrichment, separation and ionisation onto a single paper strip, and the entire analysis process, from sample to result, can be carried out in less than 10 min requiring only 2 μL of raw human saliva. PA-MS achieved a LOQ of 185 ng mL−1, mean recovery of 107 ± 7%, mean accuracy of 11 ± 8% and precision ≤5% using four concentrations, and had excellent linearity (r2 = 0.9988) in the range of 0.2–200 μg mL−1 covering the treatment concentration range, surpassing the best-in-class methods currently available for paracetamol analysis. Furthermore, from a panel of human saliva samples, inter-individual variability was found to be <10% using this approach. This technique represents a promising tool for rapid and accurate emergency diagnosis. [ABSTRACT FROM AUTHOR]

Forests play a crucial role as the primary sink for greenhouse gases, and forest mortality significantly impacts the carbon sequestration capacity of forest ecosystems. A single type of forest mortality model has been developed, and its model variables are incomplete, leading to significant bias in mortality prediction. To address this limitation, this study harnessed data collected from 773 permanent plots situated in Zhejiang Province, China, spanning a period from 2009 to 2019. The primary objectives were to pinpoint the key variables influencing forest mortality and to construct forest mortality prediction models utilizing both traditional regression methods and machine learning techniques, ultimately aiming to provide a theoretical basis for forest management practices and future predictions. Four basic linear regression models were used in this study: Linear Regression (LR), Akaike Information Criterion (AIC) Stepwise Regression, Ridge Regression, and Lasso Regression. Four machine learning models, Gradient Boosting Regression (GBR), Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP), were used to model stand mortality. Mortality was used as the dependent variable, and environmental factors such as topographic factors, soil composition, stand characteristics, and climatic variables were used as independent variables. The findings unveiled that soil and stand-related factors exerted significant effects on the mortality rate, whereas terrain-related and climate factors did not exhibit statistical significance. The Random Forest model established by using stand age, tree height, ADBH, crown cover, humus layer thickness, and the biodiversity index has the highest fitting statistics such as R² and Mean Squared Error, indicating that it has a good fitting and prediction effect, which effectively predicts mortality at the stand level, and is a valuable tool for predicting changes in forest ecosystems, with practical value in estimating tree mortality to enhance forest management and planning. [ABSTRACT FROM AUTHOR]

Fluorescence analysis using fluorescent probes as a tool is one of the most important technical methods in the field of substance detection, but fluorescent probes have problems such as poor biocompatibility and low stability. Here, metal organic frameworks (MOFs) were used to encapsulate the small molecule fluorescent probe DOX-BOD to construct the green-emitting fluorescent platform DOX-BOD@zeolitic imidazolate framework-8 (ZIF-8). The experiments show that the designed DOX-BOD@ZIF-8 has a significant fluorescence response to DOX without interference from other possible substances, with a low detection limit of 2.72 μM. In addition, DOX-BOD@ZIF-8 shows superior pH stability and photostability compared to DOX-BOD. Finally, the nanocomposite DOX-BOD@ZIF-8 was shown to be applicable for DOX detection in lake water. With the development of this type of materialized probe, there will be a significant boost to the detection and protection of the environment. [ABSTRACT FROM AUTHOR]

This study investigated the impact of formation water on the mass transfer between CO2 and crude oil in low-permeability reservoirs through CO2 miscible flooding. Formation water leads to water blocks, which affect the effectiveness of CO2 miscible flooding. Therefore, we studied the impact and mechanisms of formation water on the CO2-oil miscibility. The microscale interaction between formation water-CO2-core samples was investigated using CT scanning technology to analyze its influence on core permeability parameters. In addition, CO2 miscible flooding experiments were conducted using the core displacement method to determine the effects of formation water salinity and average water saturation on minimum miscibility pressure (MMP) and oil displacement efficiency. The CT scanning results indicate that high-salinity formation water leads to a decrease in the porosity and permeability of the core as well as pore and throat sizes under miscible pressure conditions. The experimental results of CO2 miscible flooding demonstrate that CO2-oil MMP decreases as the salinity of the formation water increases. Moreover, as the average water saturation in the core increases, the water block effect strengthens, resulting in an increase in MMP. The recovery factors of cores with average water saturations of 30%, 45%, and 60% are 89.8%, 88.6%, and 87.5%, respectively, indicating that the water block effect lowers the oil displacement efficiency and miscibility. [ABSTRACT FROM AUTHOR]

Carbon trading and carbon offset markets are potential policy options for mitigating greenhouse gas emissions and climate change. A price on carbon is expected to generate demand for carbon offsets. In a market-based framework, the carbon price should be high enough to compensate for opportunity costs. We studied a highly-modified agricultural system in the Guanzhong–Tianshui economic region of China that is typical of many temperate agricultural zones in western China. We quantified the economic returns from agriculture and from carbon plantings (both monoculture and ecological plantings) under five carbon-price scenarios. The mean carbon sequestration is 34 Mg·hm−2·a−1, and the average annual payment increased to 1146 CNY·hm−2 at a medium carbon price of 50 CNY·Mg−1 CO2−e. Thus, areas of high priority for conservation and restoration may be restored relatively cheaply in the presence of a carbon market. Overall, however, less carbon is sequestered by ecological plantings (i.e., mixed native trees and shrubs) compared to agriculture.

Papaya is one of the most consumed fruits around the world, which possesses low energy and is very suitable for those trying to reduce weight. Papaya is packed with a bunch of vitamins, minerals, as well as phenolic compounds. Phytochemicals have beneficial effects on the human body, including anti-cancer, anti-diabetic, and anti-dengue activities. Numerous studies have estimated nutritional and medicinal properties. The study of the bioaccessibility of papaya chemicals is important for both the food and medicine industries. This review summarizes the nutritional value and phytochemical content of different parts of papaya. Influencing factors like pre- and post-harvest on phytochemical content also have been reviewed. Further, the bioaccessibility and bioavailability of phytochemicals as well as the medicinal properties, based on the in vitro and in vivo studies are deeply reviewed. Finally, the research progress, gaps and interesting future aspects of papaya phytochemicals have been discussed. These phytochemical compounds are influenced by numerous factors, such as pre- and post-harvest, as well as extraction conditions. Many factors may affect the bioaccessibility and bioavailability of these phytochemicals, but there is limited information. Therefore, further studies like in vitro and in vivo experiments should be conducted to authenticate the efficiency of the papaya plant. [ABSTRACT FROM AUTHOR]